Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Value at Risk”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

International Space Station Operational Experience and Its Impacts on Future Mission Supportability

Operational experience gained on the International Space Station (ISS) has enabled significant improvements in failure rate estimates for various Orbital Replacement Units (ORUs). These improved estimates, in turn, allow more efficient and accurate spare parts allocations for future missions, enabling significant reductions in both logistics mass and risk. This paper examines the value of ISS experience to date in terms of its impact on supportability for future missions. A supportability model is presented that assesses the spares required as a function of mission endurance and risk, taking into account uncertainty in failure rate estimates. Changes in ISS Environmental Control and Life Support (ECLSS) ORU failure rate estimates are described and discussed, both in terms of the overall population of ORUs and the evolution of failure rate estimates over time for a particular item. The value of those updated failure rate estimates is assessed by calculating the estimated spares mass requirements for two cases, using the initial, pre-ISS estimates and using the estimates informed by on-orbit experience. Hidden risk resulting from underestimated failure rates is also assessed. These results indicate that, for a 1,200-day Mars mission, ISS experience has enabled a 3.9 t to 6.0 t reduction in ECLSS spares mass required and uncovered failure rate underestimates that would have resulted in an order of magnitude increase in risk had they not been discovered and corrected. The implications of these results for system development and mission planning are discussed, including approaches to accelerate the rate of failure rate refinement and the risks associated with making changes or introducing new systems. Overall, test time is a critical factor that must be carefully considered in system development, and new systems must budget appropriate time for testing in a relevant environment or accept higher risk and logistics requirements on future missions.

Owens, Andrew C.↗
NASA NTRS2015

Common Cause Failure Modeling in Space Launch Vehicles

Common Cause Failures (CCFs) are a known and documented phenomenon that defeats system redundancy. CCFs are a set of dependent type of failures that can be caused for example by system environments, manufacturing, transportation, storage, maintenance, and assembly. Since there are many factors that contribute to CCFs, they can be reduced, but are difficult to eliminate entirely. Furthermore, failure databases sometimes fail to differentiate between independent and dependent CCF. Because common cause failure data is limited in the aerospace industry, the Probabilistic Risk Assessment (PRA) Team at Bastion Technology Inc. is estimating CCF risk using generic data collected by the Nuclear Regulatory Commission (NRC). Consequently, common cause risk estimates based on this database, when applied to other industry applications, are highly uncertain. Therefore, it is important to account for a range of values for independent and CCF risk and to communicate the uncertainty to decision makers. There is an existing methodology for reducing CCF risk during design, which includes a checklist of 40+ factors grouped into eight categories. Using this checklist, an approach to produce a beta factor estimate is being investigated that quantitatively relates these factors. In this example, the checklist will be tailored to space launch vehicles, a quantitative approach will be described, and an example of the method will be presented.

Hark, Frank↗
NASA NTRS2010

Control of Risks Through the Use of Procedures: A Method for Evaluating the Change in Risk

This paper considers how procedures can be used to control risks faced by an organization and proposes a means of recognizing if a particular procedure reduces risk or contributes to the organization's exposure. The proposed method was developed out of the review of work documents and the governing procedures performed in the wake of the Columbia accident by NASA and the Space Shuttle prime contractor, United Space Alliance, LLC. A technique was needed to understand the rules, or procedural controls, in place at the time in the context of how important the role of each rule was. The proposed method assesses procedural risks, the residual risk associated with a hazard after a procedure's influence is accounted for, by considering each clause of a procedure as a unique procedural control that may be beneficial or harmful. For procedural risks with consequences severe enough to threaten the survival of the organization, the method measures the characteristics of each risk on a scale that is an alternative to the traditional consequence/likelihood couple. The dual benefits of the substitute scales are that they eliminate both the need to quantify a relationship between different consequence types and the need for the extensive history a probabilistic risk assessment would require. Control Value is used as an analog for the consequence, where the value of a rule is based on how well the control reduces the severity of the consequence when operating successfully. This value is composed of two parts: the inevitability of the consequence in the absence of the control, and the opportunity to intervene before the consequence is realized. High value controls will be ones where there is minimal need for intervention but maximum opportunity to actively prevent the outcome. Failure Likelihood is used as the substitute for the conventional likelihood of the outcome. For procedural controls, a failure is considered to be any non-malicious violation of the rule, whether intended or not. The model used for describing the Failure Likelihood considers how well a task was established by evaluating that task on five components. The components selected to define a well established task are: that it be defined, assigned to someone capable, that they be trained appropriately, that the actions be organized to enable proper completion and that some form of independent monitoring be performed. Validation of the method was based on the information provided by a group of experts in Space Shuttle ground processing when they were presented with 5 scenarios that identified a clause from a procedure. For each scenario, they recorded their perception of how important the associated rule was and how likely it was to fail. They then rated the components of Control Value and Failure Likelihood for all the scenarios. The order in which each reviewer ranked the scenarios Control Value and Failure Likelihood was compared to the order in which they ranked the scenarios for each of the associated components; inevitability and opportunity for Control Value and definition, assignment, training, organization and monitoring for Failure Likelihood. This order comparison showed how the components contributed to a relative relationship to the substitute risk element. With the relationship established for Space Shuttle ground processing, this method can be used to gauge if the introduction or removal of a particular rule will increase or decrease the .risk associated with the hazard it is intended to control.

Praino, Gregory T.↗
NASA NTRS1992

An improved approach for flight readiness certification: Methodology for failure risk assessment and application examples, volume 1

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.

Moore, N. R.↗
NASA NTRS1992

An improved approach for flight readiness certification: Methodology for failure risk assessment and application examples. Volume 2: Software documentation

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes, These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.

Moore, N. R.↗
NASA NTRS1992

An improved approach for flight readiness certification: Methodology for failure risk assessment and application examples. Volume 3: Structure and listing of programs

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.

Moore, N. R.↗
NASA NTRS1992

An improved approach for flight readiness certification: Probabilistic models for flaw propagation and turbine blade failure. Volume 1: Methodology and applications

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for designs failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented.

Moore, N. R.↗
NASA NTRS1992

An improved approach for flight readiness certification: Probabilistic models for flaw propagation and turbine blade failure. Volume 2: Software documentation

An improved methodology for quantitatively evaluating failure risk of spaceflights systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for design, failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented.

Moore, N. R.↗
DOE OSTI2020

Risk to ecological resources following remediation can be due mainly to increased resource value of successful restoration: A case study from the Department of Energy's Hanford Site

Several nations are faced with the need to remediate large contaminated sites following World War II, the Cold War, and abandoned industrial sites, and to return them to productive land uses. In the United States, the Department of Energy (DOE) has the largest cleanup challenge, and its Hanford Site in the state of Washington has the most extensive and most expensive cleanup task. Ideally, the risk to ecological resources on remediation sites is evaluated before, during, and after remediation, and the risk from, or damage to, ecological resources from contaminants should be lower following remediation. In this paper, we report the risk to ecological resources before, during, and as a consequence of remediation on contaminated units requiring cleanup, and then examine the causes for changes in risk by evaluating 56 cleanup evaluation units (EUs) at the Hanford Site. In this case, remediation includes a restoration phase. In general, the risk to ecological and eco-cultural resources is currently not discernible or low at most contaminated units, increases during remediation, and decreases thereafter. Remediation often causes physical disruption to ecosystems as it reduces the risk from exposure to contaminants. Most new remediation projects at the Hanford Site include ecological restoration. Ecological restoration results in the potential for the presence of higher quality resources after remediation than currently exists on these contaminated lands and facilities. Although counter-intuitive, our evaluation of the risk to ecological resources following remediation indicated that a significant percentage of units (61%) will be at increased risk in the post-remediation period. This increased risk is due to DOE's successful remediation and restoration that results in a higher percent of native vegetation and higher ecological value on the sites in the post-remediation period than before. These newly-created resources can then be at risk from post-remediation activities. Risks to these new higher quality resources include the potential for spread of invasive species and of noxious grasses used in previous cleanup actions, disruption of ecosystems (including those with state or federally listed species and unique ecosystems), compaction of soil, use of pesticides to control invasive species, and the eventual need for continued monitoring activities. Thus, by greatly improving the existing habitat and health of eco-receptors, and maintaining habitat corridors between high quality habitats, the ecological resources in the post-remediated units are at risk unless care is taken to protect them. Many of the negative effects of both remediation and future monitoring (or other future land uses) can be avoided by planning and management early in the remediation process. We suggest DOE and other agencies convene a panel of managers, remediation scientists, regulators, environmental and ecological scientists, Native Americans, economists, and the public to develop a generic list of performance metrics for the restoration phase of remediation, including evaluation of success, which could be applied across the DOE complex.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Toxicity Values for Chemicals

The toxicity values for chemicals contained in this dataset comprise acute, subchronic, and chronic exposure durations. Cancer slope factors, inhalation unit risk, reference dose, and reference concentrations are available. These values should be used in cancer risk and noncancer hazard assessments for the calculation of preliminary remediation goals (PRGs) and hazard characterization. Users can select toxicity values from 10 combinations of exposure durations and cancer/noncancer toxicity values and select up to 1000 chemicals per query. The dataset supports environmental risk assessments, regulatory decision-making, and environmental planning with tools for benchmarking against risk-based standards. This structured approach ensures a robust evaluation of environmental risks tailored to regulatory needs.

Stewart, Debra [Oak Ridge National Laboratory (ORN↗
DOE OSTI2021

Surrogate Modeling For Efficiently Accurately and Conservatively Estimating Measures of Risk

We present a surrogate modeling framework for conservatively estimating measures of risk from limited realizations of an expensive physical experiment or computational simulation. We adopt a probabilistic description of risk that assigns probabilities to consequences associated with an event and use risk measures, which combine objective evidence with the subjective values of decision makers, to quantify anticipated outcomes. Given a set of samples, we construct a surrogate model that produces estimates of risk measures that are always greater than their empirical estimates obtained from the training data. These surrogate models not only limit over-confidence in reliability and safety assessments, but produce estimates of risk measures that converge much faster to the true value than purely sample-based estimates. We first detail the construction of conservative surrogate models that can be tailored to the specific risk preferences of the stakeholder and then present an approach, based upon stochastic orders, for constructing surrogate models that are conservative with respect to families of risk measures. The surrogate models introduce a bias that allows them to conservatively estimate the target risk measures. We provide theoretical results that show that this bias decays at the same rate as the L 2 error in the surrogate model. Our numerical examples confirm that risk-aware surrogate models do indeed over-estimate the target risk measures while converging at the expected rate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Enabling Renewable Energy While Protecting Wildlife: An Ecological Risk-Based Approach to Wind Energy Development Using Ecosystem-Based Management Values

Acceptance of wind energy development is challenged by stakeholders’ concerns about potential effects on the environment, specifically on wildlife, such as birds, bats, and (for offshore wind) marine animals, and the habitats that support them. Communities near wind energy developments are also concerned with social and economic impacts, as well as impacts on aesthetics, historical sites, and recreation and tourism. Lack of a systematic, widely accepted, and balanced approach for measuring the potential damage to wildlife, habitats, and communities continues to leave wind developers, regulators, and other stakeholders in an uncertain position. This paper explores ecological risk-based management (RBM) in wind energy development for land-based and offshore wind installations. This paper provides a framework for the adaptation of ecosystem-based management to wind energy development and examines that framework through a series of case studies and best management practices for applying risk-based principles to wind energy. Ten case studies indicate that wind farm monitoring is often driven by regulatory requirements that may not be underpinned by scientific questions. While each case applies principles of adaptive management, there is room for improvement in applying scientific principles to the data collection and analysis. Challenges and constraints for wind farm development to meet RBM framework criteria include collecting sufficient baseline and monitoring data year-round, engaging stakeholder facilitators, and bringing together large and diverse scientific teams. The RBM framework approach may provide insights for improved siting and consenting/permitting processes for regulators and their advisors, particularly in those nations where wind energy is still in the early development stages on land or at sea.

17 WIND ENERGY↗
DOE OSTI2020

Predicting Biological Cleanliness: An Empirical Bayes Approach for Spacecraft Bioburden Accounting

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Bayesian Framework for Bioburden Density Estimation in Planetary Protection

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Cost Risk Analysis Based on Perception of the Engineering Process

In most cost estimating applications at the NASA Langley Research Center (LaRC), it is desirable to present predicted cost as a range of possible costs rather than a single predicted cost. A cost risk analysis generates a range of cost for a project and assigns a probability level to each cost value in the range. Constructing a cost risk curve requires a good estimate of the expected cost of a project. It must also include a good estimate of expected variance of the cost. Many cost risk analyses are based upon an expert's knowledge of the cost of similar projects in the past. In a common scenario, a manager or engineer, asked to estimate the cost of a project in his area of expertise, will gather historical cost data from a similar completed project. The cost of the completed project is adjusted using the perceived technical and economic differences between the two projects. This allows errors from at least three sources. The historical cost data may be in error by some unknown amount. The managers' evaluation of the new project and its similarity to the old project may be in error. The factors used to adjust the cost of the old project may not correctly reflect the differences. Some risk analyses are based on untested hypotheses about the form of the statistical distribution that underlies the distribution of possible cost. The usual problem is not just to come up with an estimate of the cost of a project, but to predict the range of values into which the cost may fall and with what level of confidence the prediction is made. Risk analysis techniques that assume the shape of the underlying cost distribution and derive the risk curve from a single estimate plus and minus some amount usually fail to take into account the actual magnitude of the uncertainty in cost due to technical factors in the project itself. This paper addresses a cost risk method that is based on parametric estimates of the technical factors involved in the project being costed. The engineering process parameters are elicited from the engineer/expert on the project and are based on that expert's technical knowledge. These are converted by a parametric cost model into a cost estimate. The method discussed makes no assumptions about the distribution underlying the distribution of possible costs, and is not tied to the analysis of previous projects, except through the expert calibrations performed by the parametric cost analyst.

Dean, Edwin B.↗
DOE OSTI2021

Gaussian Kernel Methods for Seismic Fragility and Risk Assessment of Mid-Rise Buildings

Seismic fragility functions can be evaluated using the cloud analysis method with linear regression which makes three fundamental assumptions about the relation between structural response and seismic intensity: log-linear median relationship, constant standard deviation, and Gaussian distributed errors. While cloud analysis with linear regression is a popular method, the degree to which these individual and compounded assumptions affect the fragility and the risk of mid-rise buildings needs to be systematically studied. This paper conducts such a study considering three building archetypes that make up a bulk of the building stock: RC moment frame, steel moment frame, and wood shear wall. Gaussian kernel methods are employed to capture the data-driven variations in the median structural response and standard deviation and the distributions of residuals with the intensity level. With reference to the Gaussian kernels approach, it is found that while the linear regression assumptions may not affect the fragility functions of lower damage states, this conclusion does not hold for the higher damage states (such as the Complete state). In addition, the effects of linear regression assumptions on the seismic risk are evaluated. For predicting the demand hazard, it is found that the linear regression assumptions can impact the computed risk for larger structural response values. However, for predicting the loss hazard with downtime as the decision variable, linear regression can be considered adequate for all practical purposes.

58 GEOSCIENCES↗
NASA NTRS2005

Shuttle-Derived Launch Vehicles' Capablities: An Overview

Shuttle-Derived Launch Vehicle (SDLV) concepts have been developed by a collaborative team comprising the Johnson Space Center, Marshall Space Flight Center, Kennedy Space Center, ATK-Thiokol, Lockheed Martin Space Systems Company, The Boeing Company, and United Space Alliance. The purpose of this study was to provide timely information on a full spectrum of low-risk, cost-effective options for STS-Derived Launch Vehicle concepts to support the definition of crew and cargo launch requirements for the Space Exploration Vision. Since the SDLV options use high-reliability hardware, existing facilities, and proven processes, they can provide relatively low-risk capabilities to launch extremely large payloads to low Earth orbit. This capability to reliably lift very large, high-dollar-value payloads could reduce mission operational risks by minimizing the number of complex on-orbit operations compared to architectures based on multiple smaller launchers. The SDLV options also offer several logical spiral development paths for larger exploration payloads. All of these development paths make practical and cost-effective use of existing Space Shuttle Program (SSP) hardware, infrastructure, and launch and flight operations systems. By utilizing these existing assets, the SDLV project could support the safe and orderly transition of the current SSP through the planned end of life in 2010. The SDLV concept definition work during 2004 focused on three main configuration alternatives: a side-mount heavy lifter (approximately 77 MT payload), an in-line medium lifter (approximately 22 MT Crew Exploration Vehicle payload), and an in-line heavy lifter (greater than 100 MT payload). This paper provides an overview of the configuration, performance capabilities, reliability estimates, concept of operations, and development plans for each of the various SDLV alternatives. While development, production, and operations costs have been estimated for each of the SDLV configuration alternatives, these proprietary data have not been included in this paper.

Rothschild, William J.↗